A system and method for automated customer response system mapping and duplication to provide for a common Interactive Voice Response (“IVR”) system import language or structure, comprising a contact center crawler which connects with an existing automated contact center system, may navigate and map that system by recording and selecting each available option or by interfacing with the code that makes up the IVR itself to generate a model, records the response to each selected option, and feeds the options and responses to a tree creator; a CX model generator which organizes the options and responses into a data map such as a tree or more graph-like or map-like structure, where logical conditions or operations such as loops and recursion may be present; and a coding engine that generates programmatic code representing the data model that is imported into another IVR platform for execution.
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3. The system of claim 1, wherein the data tree is converted into a structured data format for export and import into another platform, an example of such is javascript object notation (JSON) format for the purposes of reproducing the original IVR structure in another system.
4. The system of claim 1, wherein the contact center crawler, tree model generator, and CX model generator, are located on the same systems or an internal network connected to a contact center.
This invention relates to a system for analyzing and improving customer experience (CX) in contact centers. The system addresses the challenge of efficiently gathering and processing customer interaction data to generate actionable insights for enhancing service quality. The system includes a contact center crawler that collects interaction data from various sources within the contact center, such as call recordings, chat logs, and email exchanges. A tree model generator processes this data to create a hierarchical representation of customer interactions, identifying patterns and key touchpoints. A CX model generator then analyzes these patterns to generate a predictive model that evaluates the quality of customer experiences and suggests improvements. The system ensures data security and efficiency by locating the crawler, tree model generator, and CX model generator either on the same physical systems or within an internal network connected to the contact center. This centralized approach minimizes latency and enhances data integrity while providing real-time insights for contact center operations. The system enables contact centers to proactively identify issues, optimize agent performance, and refine customer service strategies based on data-driven analytics.
7. The method of claim 5, wherein the data tree is converted into a structured data format for export and import into another platform, an example of such is javascript object notation (JSON) format for the purposes of reproducing the original IVR structure in another system.
8. The method of claim 5, wherein the contact center crawler, tree model generator, and CX model generator, are located on the same systems or an internal network connected to a contact center.
This invention relates to contact center systems and methods for analyzing customer experience (CX) data. The problem addressed is the need for efficient, centralized processing of customer interaction data to generate insights and improve service quality. The system includes a contact center crawler that collects interaction data from various sources within the contact center, such as call recordings, chat logs, and email exchanges. A tree model generator processes this data to create a hierarchical structure representing customer interactions, identifying key touchpoints and patterns. A CX model generator then analyzes the structured data to generate models that assess customer experience metrics, such as satisfaction scores, resolution times, and agent performance. The contact center crawler, tree model generator, and CX model generator operate on the same systems or within an internal network connected to the contact center, ensuring secure and efficient data processing. This centralized approach allows for real-time or near-real-time analysis, enabling contact centers to quickly identify issues, optimize workflows, and enhance customer service. The system may also integrate with existing contact center infrastructure, such as CRM systems, to provide a comprehensive view of customer interactions and improve decision-making.
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August 5, 2020
November 1, 2022
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